Label-free polar metabolite quantification for untargeted metabolomics

用于非靶向代谢组学的无标记极性代谢物定量

• 批准号: 10396924
• 负责人: Thomas O Metz
• 金额: $ 21.78万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10396924
• 关键词: Calibration; Clinical; Data; Development; Experimental Designs; Goals; Industrialization; Isotope Labeling; Isotopes; Knowledge; Kynurenic Acid; Label; Legal; Mainstreaming; Measures; Methods; Reproducibility; Research; Sampling; Signal Transduction; Testing; Time; United States National Institutes of Health; Work; base; case control; data sharing; improved; interest; member; metabolomics; novel

SUMMARY The primary focus of the NIH Compound Identification Development Cores (CIDC) is to use untargeted metabolomics to not only identify novel metabolites but to facilitate and improve the identification of known metabolites. Furthermore, the CIDC is mandated to promote the accuracy, reproducibility, and interlaboratory comparison of metabolomics data. One way of promoting reproducibility, improving comparability and enhancing the confidence of metabolite identification is to improve metabolite quantification -- especially for untargeted metabolomics. Indeed, as frequently shown by untargeted NMR studies, knowledge of the concentration limits of a particular metabolite can “rule-in” or “rule-out” a tentative identification. For instance, if a metabolite signal is tentatively identified as kynurenic acid, but the measured concentration is determined to be 100X times more than normal, then that tentative identification must be incorrect and thus, “ruled out”. Traditionally compound quantification in metabolomics (especially absolute quantification) has been limited to targeted metabolomics while untargeted methods have largely relied on relative quantification. Absolute quantification by LC-MS is difficult and requires isotopically labeled standards and careful calibration. Isotopic standards are expensive and difficult to obtain. As a result, the number of metabolites that can be routinely quantified by targeted LC-MS- based methods is generally less than 500. On the other hand, relative quantification is much easier and it is possible to use peak intensity comparisons between “cases” and “controls” to relatively quantify thousands of compounds with little effort. However, relative quantification has many limitations and numerous problems. In particular, relative values cannot be compared across labs, across platforms, or even over modestly separate time periods within the same lab (batch effects). This makes relative quantification fundamentally “unFAIR” from a data sharing or reproducibility perspective. Furthermore, relative quantification only works for certain limited experimental designs (cases vs. controls) and relative values can never be used in clinical, legal or industrial test settings. This limits the application of untargeted metabolomics to “research-use only”. If untargeted metabolomics is ever going to expand beyond the lab and into the mainstream, it will need to develop robust, label-free quantification methods that can work across different samples, across platforms, across labs and across time. The challenge is how to perform metabolite quantification via LC-MS without isotopic standards? Fortunately, there have been a number of recent developments and novel ideas that integrate both experimental and computation approaches that suggest it may be possible to perform accurate metabolite quantification via untargeted LC-MS metabolomics without isotopically labeled standards. Our goal is to implement, test and refine these methods, specifically for polar metabolites, and make them available to all interested CIDC members.

摘要 NIH化合物鉴定开发核心(CIDC)的主要关注点是使用非定向的 代谢组学不仅要识别新的代谢物，而且要促进和改进已知代谢物的鉴定 代谢物。此外，CIDC的任务是促进准确性、重复性和实验室间 代谢组学数据的比较。一种促进重复性、提高可比性和增强 代谢物鉴定的可信度是为了提高代谢物的量化--特别是对于非靶向的 代谢组学。事实上，正如非靶向核磁共振研究经常表明的那样，对浓度限制的了解 一种特定的代谢物可以“排除”或“排除”一种试探性的鉴定。例如，如果一种代谢物信号 初步鉴定为犬尿酸，但测定浓度是犬尿酸的100倍 如果不是正常的，那么这个暂定的身份肯定是不正确的，因此，“排除了”。传统化合物 代谢组学中的量化(尤其是绝对量化)仅限于靶向代谢组学 而非目标方法在很大程度上依赖相对量化。LC-MS的绝对定量为 这很困难，需要同位素标记的标准和仔细的校准。同位素标准很贵，而且 很难获得。因此，可以通过靶向LC-MS常规定量的代谢物的数量。 基于方法的方法通常少于500个。另一方面，相对量化要容易得多，而且确实如此 可以使用“病例”和“对照”之间的峰值强度比较来相对量化数千个 几乎不费力气就能合成化合物。然而，相对量化有许多局限性，也存在诸多问题。在……里面 特别是，相对值不能跨实验室、跨平台进行比较，甚至不能过于分散地进行比较 同一实验室内的时间段(批处理效果)。这使得相对量化从根本上说是“不公平的” 从数据共享或再现性的角度来看。此外，相对量化只适用于某些有限的 试验设计(病例与对照)和相对值永远不能用于临床、法律或工业试验 设置。这将非靶向代谢组学的应用限制在“仅供研究使用”。如果没有目标，则 新陈代谢组学将永远超越实验室，进入主流，它将需要发展强大的， 可跨不同样本、跨平台、跨实验室和 跨越时间。挑战是如何在没有同位素标准的情况下通过LC-MS进行代谢物定量？ 幸运的是，已经有了一些最新的发展和新的想法，将这两种实验结合在一起 以及一些计算方法，这些方法表明，通过 无同位素标记标准的非靶向LC-MS代谢组学。我们的目标是实施、测试和完善 这些方法特别针对极性代谢物，并使CIDC所有感兴趣的成员都可以使用这些方法。

项目成果

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts.

• DOI: 10.1186/s13321-022-00587-7
• 发表时间: 2022-09-22
• 期刊: Journal of cheminformatics
• 影响因子: 8.6

CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.

• DOI: 10.1021/acs.analchem.1c01465
• 发表时间: 2021-08-31
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Wang, Fei;Liigand, Jaanus;Tian, Siyang;Arndt, David;Greiner, Russell;Wishart, David S.
• 通讯作者: Wishart, David S.

Mass Spectrometry Adduct Calculator.

• DOI: 10.1021/acs.jcim.1c00579
• 发表时间: 2021-12-27
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Blumer, Madison R.;Chang, Christine H.;Brayfindley, Evangelina;Nunez, Jamie R.;Colby, Sean M.;Renslow, Ryan S.;Metz, Thomas O.
• 通讯作者: Metz, Thomas O.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.

Deimos：用于处理高维质谱数据的开源工具。

• DOI: 10.1021/acs.analchem.1c05017
• 发表时间: 2022-04-26
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Colby, Sean M.;Chang, Christine H.;Bade, Jessica L.;Nunez, Jamie R.;Blumer, Madison R.;Orton, Daniel J.;Bloodsworth, Kent J.;Nakayasu, Ernesto S.;Smith, Richard D.;Ibrahim, Yehia M.;Renslow, Ryan S.;Metz, Thomas O.
• 通讯作者: Metz, Thomas O.

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification.

• DOI: 10.1093/nar/gkac383
• 发表时间: 2022-07-05
• 期刊: NUCLEIC ACIDS RESEARCH
• 影响因子: 14.9
• 作者: Wang, Fei;Allen, Dana;Tian, Siyang;Oler, Eponine;Gautam, Vasuk;Greiner, Russell;Metz, Thomas O.;Wishart, David S.
• 通讯作者: Wishart, David S.